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7 results for “Plug loads”
Codes for ''The lithospheric loading model for large impact basin where mantle plug presents''
<p>This dataset contains the fortran codes and shell script used to generate data and figure for the article titled with "<strong>The lithospheric loading model for large impact basin where mantle plug presents</strong>".</p> <p>GMT and SHTOOLS is required for the operation of these code.</p> <p>The source code named LocalizedAdmitCorrV4.f95 is modified from the SHTOOLS example: LocalizedAdmitCorr.f95 written by Mark Wieczorek (April, 2005).</p> <p> </p>
Safety, Tolerability, Plug Retention and Preliminary Efficacy of Tacrolimus-loaded Punctal Plug in Patients With Moderate to Severe Dry Eye Disease - Cohort B
ClinicalTrials.gov study NCT05618730. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Safety, Tolerability and Preliminary Efficacy of Unilateral Latanoprost-loaded Punctual Plug -EXP-LP
ClinicalTrials.gov study NCT03318146. IPD Sharing: NO. Countries: 1. Publications: 7.
Knowledge-Based System to Support Plug Load Management
Electrical plug loads comprise an increasingly larger share of building energy consumption as improvements have been made to Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems. It is anticipated that plug loads will account for a significant portion of the energy consumption of Sustainability Base, a recently constructed high-performance office building at NASA Ames Research Center. Consequently, monitoring plug loads will be critical to achieve energy efficient operations. In this paper we describe the development of a knowledge-based system to analyze data collected from a plug load management system that allows for metering and control of individual loads. Since Sustainability Base was not yet occupied at the time of this investigation, the study was conducted in another building on the Ames campus to prototype the system. The paper focuses on the knowledge engineering and verification of a modular software system that promotes efficient use of office building plug loads. The knowledge-based system generates summary usage reports and alerts building personnel of malfunctioning equipment and unexpected plug load consumption. The system is planned to be applied to Sustainability Base and is expected to identify malfunctioning loads and reduce building energy consumption.
Plug Load Data
We provide MATLAB binary files (.mat) and comma separated values files of data collected from a pilot study of a plug load management system that allows for the metering and control of individual electrical plug loads. The study included 15 power strips, each containing 4 channels (receptacles), which wirelessly transmitted power consumption data approximately once per second to 3 bridges. The bridges were connected to a building local area network which relayed data to a cloud-based service. Data were archived once per minute with the minimum, mean, and maximum power draw over each one minute interval recorded. The uncontrolled portion of the testing spanned approximately five weeks and established a baseline energy consumption. The controlled portion of the testing employed schedule-based rules for turning off selected loads during non-business hours; it also modified the energy saver policies for certain devices. Three folders are provided: “matFilesAllChOneDate” provides a MAT-file for each date, each file has all channels; “matFilesOneChAllDates” provides a MAT-file for each channel, each file has all dates; “csvFiles” provides comma separated values files for each date (note that because of data export size limitations, there are 10 csv files for each date). Each folder has the same data; there is no practical difference in content, only the way in which it is organized.
Pilot Study of a Plug Load Management System: Preparing for Sustainability Base
NASA Ames Research Center’s Sustainability Base is a new 50,000 sq. ft. high-performance office building targeting a LEED Platinum rating. Plug loads are expected to account for a significant portion of overall energy consumption because building design choices resulted in greatly reduced energy demand from Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems, which are typically major contributors to energy consumption in traditional buildings. This paper reports on a pilot study where data from a variety of plug loads were collected in a reference office building to understand usage patterns, to make a preliminary assessment as to the effectiveness of controlling (i.e., turning off and on) selected loads, and to evaluate the utility of the plug load management system chosen for the study. Findings indicate that choosing energy efficient equipment, ensuring that power saving functionality is operating effectively, promoting beneficial occupant energy behavior, and employing plug load controls to turn off equipment when not in use can lead to significant energy savings. These recommendations will be applied to Sustainability Base and further studies of plug load management systems and techniques to reduce plug energy consumption will be pursued.
Application of Inductive Monitoring System to Plug Load Anomaly Detection
NASA Ames Research Center’s Sustainability Base is a new 50,000 sq. ft. LEED Platinum office building. Plug loads are expected to account for a significant portion of the overall energy consumption. This is because building design choices have resulted in greatly reduced energy demand from Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems, which are major contributors to energy consumption in traditional buildings. In anticipation of the importance of plug loads in Sustainability Base, a pilot study was conducted to collect data from a variety of plug loads. A number of cases of anomalous or unhealthy behavior were observed including schedule-based rule failures, time-to-standby errors, changed loads, and inter-channel anomalies. These issues prevent effective plug load management; therefore, they are important to promptly identify and correct. The Inductive Monitoring System (IMS) data mining algorithm was chosen to identify errors. This paper details how an automated data analysis program was created, tested and implemented using IMS. This program will be applied to Sustainability Base to maintain effective plug load management system performance, identify malfunctioning equipment, and reduce building energy consumption.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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